Correlating nutritional values of grasses, legumes, and broadleaf weeds Shockey, W.L. 1; Rayburn, E.B.2; Basden, T 3; Seymore, D.A.4; Smith, B.D.5 1Extension Agent, West Virginia University, Kingwood, WV, 26537 2Forage Extension Specialist, West Virginia University, Morgantown, WV, 26506 3Nutrient Management Extension Specialist, West Virginia University, Morgantown, WV, 2650 4Extension Agent, West Virginia University, Franklin, WV, 26807 646 5Extension Agent, West Virginia University, Petersburg, WV, 26847 ABSTRACT INTRODUCTIONMost pastures contain grasses (gr), legumes (leg), and Climatic conditions in West Virginia are good for forage growth, CP NDFbroadleaf weeds (blw). Each class of forage has unique which can then be used as feed for ruminant livestock. Most 30 CPleg = 0.73CPgra + 11.40 70nutritional characteristics both in terms of plant pastures contain grasses, legumes, and broadleaf weeds as primary NDFleg = 0.53NDFgra + 8.02 R2 = 0.44, SDreg = 2.8composition and animal utilization. In the Appalachian forage species. Each of these forage classes has unique nutritional 60 R2 = 0.33, SDreg = 4.2 25region, gr are the dominant forage species. The growth characteristics both in terms of plant composition and animal % NDF, Leg or Blwstage of most gr, which can give an indication of its utilization. 50 % CP, Leg or Blw 20 Leg Legnutritive value, is easily identified. Experiments were In the Appalachian region of the United States grasses are the 40conducted to measure the correlation of nutritive 15 Gra Gra dominant forage species. The readily identifiable growth stage ofcomponents of gr compared to leg and blw at similar 30 grasses can give an indication of its nutritive value. To apply this Blw Blwstages of re-growth. Sixteen pastures were sampled 10 NDFblw = 0.67NDFgra + 2.83 principle to swards containing mixed classes of forage, experiments 20between May and November during a three-year period. Linear (Leg) R2 = 0.33, SDreg = 5.3 Linear (Leg) were conducted to measure the relationship between the nutritive CPblw = 1.10CPgra + 0.19After clipping, 40 samples were hand-separated 5 components of grasses to legumes and broadleaf weeds at similar R2 = 0.66, SDreg = 2.7 10according to botanical composition then analyzed for Linear (Blw) Linear (Blw) stages of re-growth.crude protein (CP), neutral detergent fiber (NDF), acid 0 0detergent fiber (ADF), and total digestible nutrients Development of a model that regresses nutritive components of 0 5 10 15 20 25 30 40 50 60 70(TDN). Correlations between gr and leg or between gr different classes of forage at similar stages of re-growth for a given % Crude Protein, Gra % NDF, Graand blw for each parameter were time and space may provide a way for ranchers and researchers toCPleg = -0.09CPgr 2 + 3.31CPgr -6.88, R2 = 0.52; predict how the different classes of plants respond to the unique environmental conditions caused by their particular grazing Figure 1 Figure 2CPblw = 1.10 CPgr + 0.19, R2 = 0.66; management strategy.NDFleg = 0.53NDFgr + 8.02, R2 = 0.33;NDFblw = 0.67NDFgr + 2.83, R2 = 0.33; ADF TDNADFleg = 0.89ADFgr – 1.62, R2 = 0.54; MATERIALS AND METHODS 45 ADFblw = 0.75ADFgra + 7.16 75 TDN = 1.20TDN – 12.50 leg gra 40 R2 = 0.52, SDreg = 3.3 70 R2 = 0.45, SDreg = 3.2 Sixteen pastures which were located on farms in several regions ofADFblw = 0.75ADFgr + 7.16, R2 = 0.52; 35 % TDN, Leg or Blw WV were managed to measure effects of weather, fertilizer, and % ADF, Leg or Blw 65 management strategies on a variety of parameters, including 30 Leg LegTDNleg = 1.27TDNgr – 16.31, R2 = 0.45; and 60 botanical composition of the swards. A series of samples was 25 Gra Gra collected by clipping 1 foot square, randomly selected areas 20TDNblw = 1.17TDNgr – 11.16, R2 = 0.36. 55 TDNblw = 1.17TDNgra – 11.20 between May and November during a three-year period. Blw Blw 15 R2 = 0.36, SDreg = 3.7Thirty-three to 66% of the variation in the nutritive ADFleg = 0.89ADFgra – 1.62 50 After clipping, 40 samples were hand-separated according to 10 R2 = 0.54, SDreg = 3.7 Linear (Leg) Linear (Leg)components in leg and blw was accounted for by botanical composition as grass (gra), legume (leg), and broadleaf 45measuring the measurements in gr at the same stage of 5 Linear (Blw) Linear (Blw) weed (blw). Separated samples were sent to the analyticalre-growth. Results suggest implications for assessing 0 40 laboratories of Dairy One, Cornell, NY and analyzed for crude proteinthe nutritive value of pasture swards by analysis of the (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), and 10 20 30 40 50 55 60 65 70gr only. total digestible nutrients (TDN) by Near Infrared Reflective (NIR) % ADF, Gra % TDN, Gra analysis. For each parameter, regressions between grasses and legumes and between grasses and broadleaf weeds were calculated by least Figure 3 Figure 4 squares regression techniques. RESULTS AND DISCUSSION SUMMARYLinear regressions between grass and legume or between grass and broadleaf weeds for each nutritional component are depicted in figures 1-4. Linear regressions accounted for 33 to 66% of the variation (with a standardLegumes contained higher concentrations of CP than both the grasses and broadleaf weeds (Figure 1). It was also noted that changes in the concentration of CP in the deviation about the regression of 2.7 to 5.3 units) in the nutritive componentsgrasses were more closely related to changes in the broadleaf weeds compared to legumes. in legumes and broadleaf weeds by inputting the components in grass overNDF was lower in both legumes and broadleaf weeds compared to grasses and were positively regressed (Figure 2). ADF levels were lower in legumes compared to grasses the same range of re-growth. These results suggest implications forand broadleaf weeds (Figure 3). predicting animal performance based on the botanical components of pasture swards. A larger and more comprehensive database could improveTDN was similar over the range measured among all the 3 classes of forage (Figure 4). This supports the concept of a similar energy value across all classes of forages with the precision of regressions between botanical composition and animalperformance parameters being more closely related to intake than utilization. This observation is consistent with the results of Weiss, W.P. and Shockey, W.L. (1992. performance and provide criteria for pasture managers to maximize forageOrchardgrass can be a good forage for dairy cows. Hoards Dairyman, 137(5) , p 204) who noted that the performance of lactating dairy cows was similar for cows utilization.consuming orchardgrass vs alfalfa because the NDF was more digestible in the grass compared to the legume.
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